In a multi-agent resource race, independent Q-learning agents alternate less often than random policies, while conventional fairness/efficiency metrics hide the deficit.
Overcoming inefficient lock-in in coordination games with so- phisticated and myopic players,
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The Coordination Gap: Multi-Agent Alternation Metrics for Temporal Fairness in Repeated Games
In a multi-agent resource race, independent Q-learning agents alternate less often than random policies, while conventional fairness/efficiency metrics hide the deficit.